This function trains the a machine learning model on the training data, using a num.folds-fold internal cross-validation scheme to find the optimal hyper-parameters of the model.
plm.trainer(feat, label, cl = "classif.cvglmnet", data.split = NULL, stratify = TRUE, modsel.crit = "auc", min.nonzero.coeff = 1)
| feat | features object |
|---|---|
| label | label object |
| cl | class of learner, directly passed to makeLearner |
| data.split | filename containing the training samples or list of training instances produced by data.splitter(), defaults to |
| stratify | boolean, should the folds in the internal cross-validation be stratified? |
| min.nonzero.coeff | integer number of minimum nonzero coefficients that should be present in the model |
an object of class makeWrappedModel